Magnetic Resonance (MR) imaging provides excellent soft tissue contrast. Therefore, it is often used for diagnosing neurological diseases. Radiologists are trained to identify subtle changes in the appearance of tissues in MR images and, for example, use this information for differential diagnosis. For example, MR imaging is an important tool for diagnosing Multiple Sclerosis (MS) and monitoring a patient's response to therapy.
As a disease state of brain tissues changes, their appearance in an MR image—image “texture”—also changes. Image texture is quantified using, for example, space-frequency transforms. Space-frequency transforms quantitatively describe the magnitude of each spatial frequency component present in an image. Changes in image texture are then quantified by determining the corresponding changes in spatial frequency.
Reference Zhu H, Goodyear B G, Lauzon M L, Brown R A, Mayer G S, Law A G. Mansinha L, Mitchell R: “A new local multiscale Fourier analysis for medical imaging”, Med Phys, Vol. 30, pp 1134-1141, 2003 teaches use of a space-frequency transform—the Stockwell Transform (ST) to determine correlations between changes in a broad range of spatial frequencies in MR images and the state of neurological diseases such as brain cancer and MS. Local spectra have been determined for small Regions Of Interest (ROIs) and differences in amplitude of the corresponding spectral components have been analyzed. For example, spectral components have been identified that discriminate between two genetic sub-types of brain tumors: one that is chemo-sensitive and one that is chemo-resistant. Furthermore, texture analysis studies on humans and animals have shown that spectral differences in MR images are associated with MS lesion evolution pathology. Results from these studies show that spatial frequency information provides a sensitive and specific indication of disease activity.
However, the clinical utility of ST-based image texture analysis is limited due to extensive data processing and data storage. Consequently, ST-based image texture analysis is often limited to a small ROI within the image that contains an area of suspected pathology. However, spatial frequency resolution is inversely proportional to the Field-Of-View (FOV) of the ROI being examined: Δk=1/FOV. Therefore, the analysis of small ROIs has the drawback of poor spatial frequency resolution. Poor spatial frequency resolution substantially limits the ability to examine subtle spectral changes correlating with disease progression. Furthermore, small ROIs limit the ability to analyze widespread sub-clinical abnormalities and to identify changes before they are visually apparent in conventional MR images.
It is desirable to provide a method for texture quantification of image data using a space-frequency transform that is efficient with regard to data processing and data storage.
It is also desirable to provide a method for texture quantification of image data using a space-frequency transform that enables texture analysis having sufficient spatial frequency resolution.